Atomic scale characterization of precipitates in an Al-Si-Mg alloy containing excess Si and trace amounts of Cu
Bibliographic record
Abstract
The precipitation kinetics of a foundry Al-Si-Mg-(Cu) alloy containing excess Si and impurity levels of Cu were investigated on the atomic scale. A combination of APT, STEM and DSC analysis was used to characterize the evolution of chemical composition and crystal structure of the precipitates during artificial aging. It was observed that even at trace levels (0.02 wt. %), Cu has the capability to alter the precipitation sequence of the Al-Si-Mg alloy, where instead of pure β ″, hybrid phases containing unit cells of β ″, B′ and Q′ form during age hardening of the alloy in large number densities. APT investigation confirmed the incorporation of Cu in precipitates during peak-aging and its enrichment to maximum values during over-aging. Observance of more Q′ unit cells in the hybrid phases after over-aging was related to this Cu enrichment. Coarse Cu-free U1 phases nucleating predominantly on grain-boundaries were observed in the over-aged condition. Interfacial energies were calculated to explain the large number densities of the hybrid B′/Q′ precipitates and coarsening of the U1 phases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".